Source code for transformers.models.gpt2.configuration_gpt2

# coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
""" OpenAI GPT-2 configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging

logger = logging.get_logger(__name__)

    "gpt2": "",
    "gpt2-medium": "",
    "gpt2-large": "",
    "gpt2-xl": "",
    "distilgpt2": "",

[docs]class GPT2Config(PretrainedConfig): """ This is the configuration class to store the configuration of a :class:`~transformers.GPT2Model` or a :class:`~transformers.TFGPT2Model`. It is used to instantiate a GPT-2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the GPT-2 `small <>`__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 50257): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.GPT2Model` or :class:`~transformers.TFGPT2Model`. n_positions (:obj:`int`, `optional`, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). n_ctx (:obj:`int`, `optional`, defaults to 1024): Dimensionality of the causal mask (usually same as n_positions). n_embd (:obj:`int`, `optional`, defaults to 768): Dimensionality of the embeddings and hidden states. n_layer (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. n_inner (:obj:`int`, `optional`, defaults to None): Dimensionality of the inner feed-forward layers. :obj:`None` will set it to 4 times n_embd activation_function (:obj:`str`, `optional`, defaults to :obj:`"gelu"`): Activation function, to be selected in the list :obj:`["relu", "silu", "gelu", "tanh", "gelu_new"]`. resid_pdrop (:obj:`float`, `optional`, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (:obj:`int`, `optional`, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5): The epsilon to use in the layer normalization layers initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. summary_type (:obj:`string`, `optional`, defaults to :obj:`"cls_index"`): Argument used when doing sequence summary, used in the models :class:`~transformers.GPT2DoubleHeadsModel` and :class:`~transformers.TFGPT2DoubleHeadsModel`. Has to be one of the following options: - :obj:`"last"`: Take the last token hidden state (like XLNet). - :obj:`"first"`: Take the first token hidden state (like BERT). - :obj:`"mean"`: Take the mean of all tokens hidden states. - :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - :obj:`"attn"`: Not implemented now, use multi-head attention. summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`): Argument used when doing sequence summary, used in the models :class:`~transformers.GPT2DoubleHeadsModel` and :class:`~transformers.TFGPT2DoubleHeadsModel`. Whether or not to add a projection after the vector extraction. summary_activation (:obj:`str`, `optional`): Argument used when doing sequence summary. Used in for the multiple choice head in :class:`~transformers.GPT2DoubleHeadsModel`. Pass :obj:`"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (:obj:`bool`, `optional`, defaults to :obj:`True`): Argument used when doing sequence summary, used in the models :class:`~transformers.GPT2DoubleHeadsModel` and :class:`~transformers.TFGPT2DoubleHeadsModel`. Whether the projection outputs should have :obj:`config.num_labels` or :obj:`config.hidden_size` classes. summary_first_dropout (:obj:`float`, `optional`, defaults to 0.1): Argument used when doing sequence summary, used in the models :class:`~transformers.GPT2DoubleHeadsModel` and :class:`~transformers.TFGPT2DoubleHeadsModel`. The dropout ratio to be used after the projection and activation. gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last key/values attentions (not used by all models). Example:: >>> from transformers import GPT2Model, GPT2Config >>> # Initializing a GPT2 configuration >>> configuration = GPT2Config() >>> # Initializing a model from the configuration >>> model = GPT2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "gpt2" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=50257, n_positions=1024, n_ctx=1024, n_embd=768, n_layer=12, n_head=12, n_inner=None, activation_function="gelu_new", resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, summary_type="cls_index", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, gradient_checkpointing=False, use_cache=True, bos_token_id=50256, eos_token_id=50256, **kwargs ): super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.n_ctx = n_ctx self.n_positions = n_positions self.n_embd = n_embd self.n_layer = n_layer self.n_head = n_head self.n_inner = n_inner self.activation_function = activation_function self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attn_pdrop = attn_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_first_dropout = summary_first_dropout self.summary_proj_to_labels = summary_proj_to_labels self.gradient_checkpointing = gradient_checkpointing self.use_cache = use_cache self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id @property def max_position_embeddings(self): return self.n_positions @property def hidden_size(self): return self.n_embd @property def num_attention_heads(self): return self.n_head @property def num_hidden_layers(self): return self.n_layer